Overview

Brought to you by YData

Dataset statistics

Number of variables56
Number of observations929
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory368.5 KiB
Average record size in memory406.1 B

Variable types

Categorical41
Numeric9
Boolean6

Alerts

Bathroom_0 is highly overall correlated with Bathroom_above 2 and 2 other fieldsHigh correlation
Bathroom_1 is highly overall correlated with UnitNumber and 1 other fieldsHigh correlation
Bathroom_2 is highly overall correlated with has_security_features and 1 other fieldsHigh correlation
Bathroom_above 2 is highly overall correlated with Bathroom_0 and 1 other fieldsHigh correlation
Bedroom_0 is highly overall correlated with Is Price/Sqm outlier and 11 other fieldsHigh correlation
Bedroom_1 is highly overall correlated with has_modern and 3 other fieldsHigh correlation
Bedroom_2 is highly overall correlated with log(sqm)High correlation
Bedroom_3 is highly overall correlated with nameHigh correlation
BuildingNumber is highly overall correlated with Is BuildingNumber outlier and 2 other fieldsHigh correlation
FloorNumber is highly overall correlated with HighFloor and 3 other fieldsHigh correlation
HighFloor is highly overall correlated with FloorNumber and 4 other fieldsHigh correlation
Is BuildingNumber outlier is highly overall correlated with BuildingNumber and 2 other fieldsHigh correlation
Is FloorNumber outlier is highly overall correlated with FloorNumber and 4 other fieldsHigh correlation
Is Price(EGP) outlier is highly overall correlated with Is Price/Sqm outlier and 5 other fieldsHigh correlation
Is Price/Sqm outlier is highly overall correlated with Bedroom_0 and 10 other fieldsHigh correlation
Is UnitNumber outlier is highly overall correlated with BuildingNumber and 3 other fieldsHigh correlation
Is sqm outlier is highly overall correlated with city_Mansoura and 4 other fieldsHigh correlation
LowFloor is highly overall correlated with FloorNumber and 1 other fieldsHigh correlation
MidFloor is highly overall correlated with FloorNumber and 1 other fieldsHigh correlation
Price(EGP) is highly overall correlated with Is Price(EGP) outlier and 10 other fieldsHigh correlation
Price/Sqm is highly overall correlated with Bedroom_0 and 16 other fieldsHigh correlation
UnitNumber is highly overall correlated with Bathroom_1 and 1 other fieldsHigh correlation
city_Cairo is highly overall correlated with Bedroom_0 and 7 other fieldsHigh correlation
city_Mansoura is highly overall correlated with Is sqm outlier and 6 other fieldsHigh correlation
city_Mersa Matruh is highly overall correlated with nameHigh correlation
city_New Administrative Capital is highly overall correlated with BuildingNumber and 2 other fieldsHigh correlation
city_New Cairo is highly overall correlated with Price(EGP) and 7 other fieldsHigh correlation
city_North Coast is highly overall correlated with governorate_Alexandria and 5 other fieldsHigh correlation
city_Old Cairo is highly overall correlated with log(price) and 1 other fieldsHigh correlation
governorate_Alexandria is highly overall correlated with city_North Coast and 1 other fieldsHigh correlation
governorate_Cairo is highly overall correlated with Bedroom_0 and 4 other fieldsHigh correlation
governorate_Dakahlia is highly overall correlated with Is sqm outlier and 6 other fieldsHigh correlation
governorate_Matrouh is highly overall correlated with Bathroom_0 and 4 other fieldsHigh correlation
has_building_facilities is highly overall correlated with name and 1 other fieldsHigh correlation
has_facilities is highly overall correlated with nameHigh correlation
has_green_view is highly overall correlated with nameHigh correlation
has_main_facilities is highly overall correlated with log(price/sqm) and 1 other fieldsHigh correlation
has_modern is highly overall correlated with Bedroom_1 and 1 other fieldsHigh correlation
has_other_features is highly overall correlated with city_North Coast and 1 other fieldsHigh correlation
has_parking_safety is highly overall correlated with Price/Sqm and 3 other fieldsHigh correlation
has_security_features is highly overall correlated with Bathroom_2 and 2 other fieldsHigh correlation
has_water_feature is highly overall correlated with governorate_Matrouh and 2 other fieldsHigh correlation
log(price) is highly overall correlated with Bedroom_0 and 14 other fieldsHigh correlation
log(price/sqm) is highly overall correlated with Bedroom_0 and 17 other fieldsHigh correlation
log(sqm) is highly overall correlated with Bedroom_1 and 15 other fieldsHigh correlation
name is highly overall correlated with Bathroom_0 and 45 other fieldsHigh correlation
propertyCategory_Commercial is highly overall correlated with Bedroom_0 and 12 other fieldsHigh correlation
propertyCategory_Residential is highly overall correlated with Bedroom_0 and 12 other fieldsHigh correlation
propertySubType_Apartment (Residential - Multi-Story Units) is highly overall correlated with Bedroom_0 and 9 other fieldsHigh correlation
propertySubType_Office is highly overall correlated with Bedroom_0 and 9 other fieldsHigh correlation
propertySubType_Others(very rare) is highly overall correlated with log(sqm) and 1 other fieldsHigh correlation
propertySubType_Residential - Attached Houses is highly overall correlated with city_Mansoura and 5 other fieldsHigh correlation
propertySubType_Retail is highly overall correlated with Bedroom_0 and 6 other fieldsHigh correlation
sqm is highly overall correlated with Bedroom_1 and 9 other fieldsHigh correlation
year_built is highly overall correlated with city_Cairo and 6 other fieldsHigh correlation
has_security_features is highly imbalanced (59.5%)Imbalance
HighFloor is highly imbalanced (57.7%)Imbalance
Bedroom_1 is highly imbalanced (55.5%)Imbalance
Bathroom_1 is highly imbalanced (58.0%)Imbalance
Bathroom_2 is highly imbalanced (68.4%)Imbalance
city_Cairo is highly imbalanced (65.0%)Imbalance
city_Mansoura is highly imbalanced (65.5%)Imbalance
city_New Cairo is highly imbalanced (60.7%)Imbalance
city_Old Cairo is highly imbalanced (84.4%)Imbalance
governorate_Dakahlia is highly imbalanced (65.5%)Imbalance
propertySubType_Office is highly imbalanced (57.3%)Imbalance
propertySubType_Others(very rare) is highly imbalanced (88.7%)Imbalance
propertySubType_Residential - Attached Houses is highly imbalanced (65.5%)Imbalance
propertySubType_Retail is highly imbalanced (63.0%)Imbalance
Is sqm outlier is highly imbalanced (60.3%)Imbalance
Is FloorNumber outlier is highly imbalanced (70.7%)Imbalance
FloorNumber has 193 (20.8%) zerosZeros

Reproduction

Analysis started2025-10-17 14:57:41.741206
Analysis finished2025-10-17 14:58:05.384652
Duration23.64 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

name
Categorical

High correlation 

Distinct29
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Alamain (Latin District)
204 
Latin City
144 
Mazarine Apartment
106 
Zahya
60 
Central
52 
Other values (24)
363 

Length

Max length38
Median length33
Mean length14.883746
Min length4

Characters and Unicode

Total characters13,827
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.6%

Sample

1st rowAlamain (Latin District)
2nd rowBeachfront Tower - B1
3rd rowPODIA
4th rowMazarine Apartment
5th rowAlamain (Latin District)

Common Values

ValueCountFrequency (%)
Alamain (Latin District)204
22.0%
Latin City144
15.5%
Mazarine Apartment106
11.4%
Zahya60
 
6.5%
Central52
 
5.6%
New Garden City52
 
5.6%
PODIA43
 
4.6%
Jade Park35
 
3.8%
Beachfront Tower - B135
 
3.8%
Downtown Extension29
 
3.1%
Other values (19)169
18.2%

Length

2025-10-17T17:58:05.510022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
latin348
17.0%
alamain204
 
10.0%
district204
 
10.0%
city196
 
9.6%
mazarine119
 
5.8%
apartment106
 
5.2%
zahya60
 
2.9%
park57
 
2.8%
central52
 
2.5%
garden52
 
2.5%
Other values (37)651
31.8%

Most occurring characters

ValueCountFrequency (%)
a1610
 
11.6%
t1396
 
10.1%
i1395
 
10.1%
n1125
 
8.1%
1121
 
8.1%
r734
 
5.3%
e730
 
5.3%
A432
 
3.1%
s404
 
2.9%
m388
 
2.8%
Other values (43)4492
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)13827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1610
 
11.6%
t1396
 
10.1%
i1395
 
10.1%
n1125
 
8.1%
1121
 
8.1%
r734
 
5.3%
e730
 
5.3%
A432
 
3.1%
s404
 
2.9%
m388
 
2.8%
Other values (43)4492
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1610
 
11.6%
t1396
 
10.1%
i1395
 
10.1%
n1125
 
8.1%
1121
 
8.1%
r734
 
5.3%
e730
 
5.3%
A432
 
3.1%
s404
 
2.9%
m388
 
2.8%
Other values (43)4492
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1610
 
11.6%
t1396
 
10.1%
i1395
 
10.1%
n1125
 
8.1%
1121
 
8.1%
r734
 
5.3%
e730
 
5.3%
A432
 
3.1%
s404
 
2.9%
m388
 
2.8%
Other values (43)4492
32.5%

sqm
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.6346
Minimum15
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:05.685667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile88.094
Q1141
median205
Q3240
95-th percentile398
Maximum990
Range975
Interquartile range (IQR)99

Descriptive statistics

Standard deviation111.86272
Coefficient of variation (CV)0.52856539
Kurtosis9.3942664
Mean211.6346
Median Absolute Deviation (MAD)47
Skewness2.2921712
Sum196608.54
Variance12513.269
MonotonicityNot monotonic
2025-10-17T17:58:05.852235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.8652
 
5.6%
25231
 
3.3%
20727
 
2.9%
23121
 
2.3%
39814
 
1.5%
13413
 
1.4%
176.5213
 
1.4%
41012
 
1.3%
17611
 
1.2%
13011
 
1.2%
Other values (381)724
77.9%
ValueCountFrequency (%)
151
 
0.1%
22.51
 
0.1%
271
 
0.1%
341
 
0.1%
361
 
0.1%
371
 
0.1%
392
0.2%
421
 
0.1%
44.51
 
0.1%
453
0.3%
ValueCountFrequency (%)
9901
 
0.1%
8911
 
0.1%
8601
 
0.1%
8501
 
0.1%
773.71
 
0.1%
7404
0.4%
7241
 
0.1%
6921
 
0.1%
6821
 
0.1%
610.91
 
0.1%

year_built
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2025
664 
2022
184 
2024
 
65
2023
 
16

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3,716
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025
2nd row2025
3rd row2025
4th row2025
5th row2025

Common Values

ValueCountFrequency (%)
2025664
71.5%
2022184
 
19.8%
202465
 
7.0%
202316
 
1.7%

Length

2025-10-17T17:58:05.991648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:06.078060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025664
71.5%
2022184
 
19.8%
202465
 
7.0%
202316
 
1.7%

Most occurring characters

ValueCountFrequency (%)
22042
55.0%
0929
25.0%
5664
 
17.9%
465
 
1.7%
316
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22042
55.0%
0929
25.0%
5664
 
17.9%
465
 
1.7%
316
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22042
55.0%
0929
25.0%
5664
 
17.9%
465
 
1.7%
316
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22042
55.0%
0929
25.0%
5664
 
17.9%
465
 
1.7%
316
 
0.4%

Price(EGP)
Real number (ℝ)

High correlation 

Distinct795
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13136736
Minimum1208000
Maximum1.21123 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:06.217100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1208000
5-th percentile3010047.6
Q16812000
median9814000
Q313399000
95-th percentile38590600
Maximum1.21123 × 108
Range1.19915 × 108
Interquartile range (IQR)6587000

Descriptive statistics

Standard deviation11962736
Coefficient of variation (CV)0.91063231
Kurtosis12.425376
Mean13136736
Median Absolute Deviation (MAD)3255000
Skewness2.9439601
Sum1.2204027 × 1010
Variance1.4310705 × 1014
MonotonicityNot monotonic
2025-10-17T17:58:06.395767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29451549
 
1.0%
77970006
 
0.6%
113950004
 
0.4%
49000004
 
0.4%
75360004
 
0.4%
27186044
 
0.4%
26682594
 
0.4%
65050003
 
0.3%
40230003
 
0.3%
61180003
 
0.3%
Other values (785)885
95.3%
ValueCountFrequency (%)
12080001
0.1%
16030001
0.1%
16490001
0.1%
16630001
0.1%
18100001
0.1%
20730001
0.1%
22800001
0.1%
23470001
0.1%
23500001
0.1%
23830001
0.1%
ValueCountFrequency (%)
1211230001
0.1%
789470001
0.1%
666020001
0.1%
664770001
0.1%
662280002
0.2%
661040001
0.1%
656060001
0.1%
653570002
0.2%
652320001
0.1%
567250001
0.1%

Price/Sqm
Real number (ℝ)

High correlation 

Distinct818
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69685.497
Minimum14620.32
Maximum518794.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:06.583212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14620.32
5-th percentile24014.791
Q132634.615
median48369.942
Q364921.951
95-th percentile208840.22
Maximum518794.87
Range504174.55
Interquartile range (IQR)32287.336

Descriptive statistics

Standard deviation64312.998
Coefficient of variation (CV)0.92290362
Kurtosis7.3722254
Mean69685.497
Median Absolute Deviation (MAD)15746.511
Skewness2.5361859
Sum64737827
Variance4.1361617 × 109
MonotonicityNot monotonic
2025-10-17T17:58:06.751753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22655.03089
 
1.0%
32623.4316
 
0.6%
1027004
 
0.4%
22655.03334
 
0.4%
32623.37664
 
0.4%
31612.90324
 
0.4%
24084.62513
 
0.3%
24086.61483
 
0.3%
42957.44683
 
0.3%
32288.00693
 
0.3%
Other values (808)886
95.4%
ValueCountFrequency (%)
14620.32021
 
0.1%
15025.29821
 
0.1%
19491.70791
 
0.1%
20198.41271
 
0.1%
20525.06921
 
0.1%
21221.16471
 
0.1%
21601.19051
 
0.1%
21815.95832
0.2%
22235.49091
 
0.1%
22235.49173
0.3%
ValueCountFrequency (%)
518794.87181
0.1%
386729.16851
0.1%
378444.44441
0.1%
370486.95651
0.1%
343147.16981
0.1%
333745.76271
0.1%
331674.671
0.1%
331044.77611
0.1%
329080.56871
0.1%
327767.47831
0.1%

has_main_facilities
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
706 
0
223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

Length

2025-10-17T17:58:06.890787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:06.968935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

Most occurring characters

ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1706
76.0%
0223
 
24.0%

has_security_features
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
854 
0
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

Length

2025-10-17T17:58:07.065508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:07.150905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

Most occurring characters

ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1854
91.9%
075
 
8.1%

has_parking_safety
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
823 
1
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

Length

2025-10-17T17:58:07.241870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:07.316195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0823
88.6%
1106
 
11.4%

has_building_facilities
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
583 
1
346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0583
62.8%
1346
37.2%

Length

2025-10-17T17:58:07.411103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:07.485977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0583
62.8%
1346
37.2%

Most occurring characters

ValueCountFrequency (%)
0583
62.8%
1346
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0583
62.8%
1346
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0583
62.8%
1346
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0583
62.8%
1346
37.2%

has_other_features
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
631 
0
298 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1631
67.9%
0298
32.1%

Length

2025-10-17T17:58:07.581980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:07.662204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1631
67.9%
0298
32.1%

Most occurring characters

ValueCountFrequency (%)
1631
67.9%
0298
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1631
67.9%
0298
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1631
67.9%
0298
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1631
67.9%
0298
32.1%

HighFloor
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
849 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

Length

2025-10-17T17:58:07.758337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:07.834496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0849
91.4%
180
 
8.6%

LowFloor
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
513 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0513
55.2%
1416
44.8%

Length

2025-10-17T17:58:07.941196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:08.019024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0513
55.2%
1416
44.8%

Most occurring characters

ValueCountFrequency (%)
0513
55.2%
1416
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0513
55.2%
1416
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0513
55.2%
1416
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0513
55.2%
1416
44.8%

MidFloor
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
496 
1
433 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0496
53.4%
1433
46.6%

Length

2025-10-17T17:58:08.116567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:08.194034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0496
53.4%
1433
46.6%

Most occurring characters

ValueCountFrequency (%)
0496
53.4%
1433
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0496
53.4%
1433
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0496
53.4%
1433
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0496
53.4%
1433
46.6%

FloorNumber
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1054898
Minimum0
Maximum37
Zeros193
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:08.287369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile12
Maximum37
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.4987912
Coefficient of variation (CV)1.3393752
Kurtosis18.055289
Mean4.1054898
Median Absolute Deviation (MAD)2
Skewness3.7588022
Sum3814
Variance30.236705
MonotonicityNot monotonic
2025-10-17T17:58:08.421099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0193
20.8%
1114
12.3%
2109
11.7%
5108
11.6%
393
10.0%
492
9.9%
674
 
8.0%
766
 
7.1%
813
 
1.4%
3712
 
1.3%
Other values (18)55
 
5.9%
ValueCountFrequency (%)
0193
20.8%
1114
12.3%
2109
11.7%
393
10.0%
492
9.9%
5108
11.6%
674
 
8.0%
766
 
7.1%
813
 
1.4%
98
 
0.9%
ValueCountFrequency (%)
3712
1.3%
361
 
0.1%
331
 
0.1%
281
 
0.1%
261
 
0.1%
251
 
0.1%
242
 
0.2%
231
 
0.1%
212
 
0.2%
204
 
0.4%

BuildingNumber
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.395048
Minimum1
Maximum883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:08.575846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q317
95-th percentile645
Maximum883
Range882
Interquartile range (IQR)13

Descriptive statistics

Standard deviation181.15279
Coefficient of variation (CV)2.6104569
Kurtosis8.3061856
Mean69.395048
Median Absolute Deviation (MAD)4
Skewness3.0571592
Sum64468
Variance32816.332
MonotonicityNot monotonic
2025-10-17T17:58:08.745169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6135
14.5%
3110
 
11.8%
797
 
10.4%
179
 
8.5%
445
 
4.8%
236
 
3.9%
1234
 
3.7%
1333
 
3.6%
1028
 
3.0%
72224
 
2.6%
Other values (69)308
33.2%
ValueCountFrequency (%)
179
8.5%
236
 
3.9%
3110
11.8%
445
 
4.8%
518
 
1.9%
6135
14.5%
797
10.4%
815
 
1.6%
911
 
1.2%
1028
 
3.0%
ValueCountFrequency (%)
88310
1.1%
72224
2.6%
64518
1.9%
4252
 
0.2%
4241
 
0.1%
4234
 
0.4%
4225
 
0.5%
4201
 
0.1%
4181
 
0.1%
4174
 
0.4%

UnitNumber
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62299.854
Minimum1
Maximum722712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:08.915186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median64
Q31405
95-th percentile616162.6
Maximum722712
Range722711
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation166005.99
Coefficient of variation (CV)2.6646289
Kurtosis6.2465783
Mean62299.854
Median Absolute Deviation (MAD)62
Skewness2.7347675
Sum57876564
Variance2.7557989 × 1010
MonotonicityNot monotonic
2025-10-17T17:58:09.084554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5108
 
11.6%
3106
 
11.4%
266
 
7.1%
123
 
2.5%
716
 
1.7%
414
 
1.5%
612
 
1.3%
910
 
1.1%
1210
 
1.1%
1010
 
1.1%
Other values (451)554
59.6%
ValueCountFrequency (%)
123
 
2.5%
266
7.1%
3106
11.4%
414
 
1.5%
5108
11.6%
612
 
1.3%
716
 
1.7%
85
 
0.5%
910
 
1.1%
1010
 
1.1%
ValueCountFrequency (%)
7227121
0.1%
7226121
0.1%
7224111
0.1%
7223111
0.1%
6653041
0.1%
6652041
0.1%
6617021
0.1%
6613061
0.1%
6612061
0.1%
6607041
0.1%

has_modern
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
739 
1
190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

Length

2025-10-17T17:58:09.247498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:09.324718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

Most occurring characters

ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0739
79.5%
1190
 
20.5%

has_facilities
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
494 
0
435 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1494
53.2%
0435
46.8%

Length

2025-10-17T17:58:09.437202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:09.515000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1494
53.2%
0435
46.8%

Most occurring characters

ValueCountFrequency (%)
1494
53.2%
0435
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1494
53.2%
0435
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1494
53.2%
0435
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1494
53.2%
0435
46.8%

has_green_view
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
522 
1
407 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0522
56.2%
1407
43.8%

Length

2025-10-17T17:58:09.615515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:09.693617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0522
56.2%
1407
43.8%

Most occurring characters

ValueCountFrequency (%)
0522
56.2%
1407
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0522
56.2%
1407
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0522
56.2%
1407
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0522
56.2%
1407
43.8%

has_water_feature
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
539 
1
390 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Length

2025-10-17T17:58:09.789785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:09.868691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Most occurring characters

ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0539
58.0%
1390
42.0%

Bedroom_0
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
747 
1
182 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Length

2025-10-17T17:58:09.971923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.051614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0747
80.4%
1182
 
19.6%

Bedroom_1
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
843 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Length

2025-10-17T17:58:10.149725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.239183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0843
90.7%
186
 
9.3%

Bedroom_2
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
794 
1
135 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Length

2025-10-17T17:58:10.334327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.409039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0794
85.5%
1135
 
14.5%

Bedroom_3
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
536 
1
393 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Length

2025-10-17T17:58:10.499970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.574520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Most occurring characters

ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0536
57.7%
1393
42.3%

Bedroom_above 3
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
796 
1
133 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Length

2025-10-17T17:58:10.668136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.744447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0796
85.7%
1133
 
14.3%

Bathroom_0
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
613 
0
316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Length

2025-10-17T17:58:10.838602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:10.914970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Most occurring characters

ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1613
66.0%
0316
34.0%

Bathroom_1
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
850 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Length

2025-10-17T17:58:11.024225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.102669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0850
91.5%
179
 
8.5%

Bathroom_2
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
876 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Length

2025-10-17T17:58:11.198359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.275060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0876
94.3%
153
 
5.7%

Bathroom_above 2
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
745 
1
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

Length

2025-10-17T17:58:11.363766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.436967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0745
80.2%
1184
 
19.8%

city_Cairo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
868 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

Length

2025-10-17T17:58:11.538449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.616750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0868
93.4%
161
 
6.6%

city_Mansoura
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
869 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Length

2025-10-17T17:58:11.706030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.794056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

city_Mersa Matruh
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
812 
1
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

Length

2025-10-17T17:58:11.880918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:11.953681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0812
87.4%
1117
 
12.6%

city_New Administrative Capital
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
791 
1
138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

Length

2025-10-17T17:58:12.052143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.130781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0791
85.1%
1138
 
14.9%

city_New Cairo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
857 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

Length

2025-10-17T17:58:12.224817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.297438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0857
92.2%
172
 
7.8%

city_North Coast
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
469 
1
460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0469
50.5%
1460
49.5%

Length

2025-10-17T17:58:12.385880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.462105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0469
50.5%
1460
49.5%

Most occurring characters

ValueCountFrequency (%)
0469
50.5%
1460
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0469
50.5%
1460
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0469
50.5%
1460
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0469
50.5%
1460
49.5%

city_Old Cairo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
908 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

Length

2025-10-17T17:58:12.575546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.653246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0908
97.7%
121
 
2.3%

governorate_Alexandria
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
731 
1
198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

Length

2025-10-17T17:58:12.744194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.822064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0731
78.7%
1198
 
21.3%

governorate_Cairo
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
637 
1
292 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0637
68.6%
1292
31.4%

Length

2025-10-17T17:58:12.916585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:12.995683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0637
68.6%
1292
31.4%

Most occurring characters

ValueCountFrequency (%)
0637
68.6%
1292
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0637
68.6%
1292
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0637
68.6%
1292
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0637
68.6%
1292
31.4%

governorate_Dakahlia
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
869 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Length

2025-10-17T17:58:13.097658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:13.174467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

governorate_Matrouh
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
550 
1
379 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0550
59.2%
1379
40.8%

Length

2025-10-17T17:58:13.266995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:13.358105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0550
59.2%
1379
40.8%

Most occurring characters

ValueCountFrequency (%)
0550
59.2%
1379
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0550
59.2%
1379
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0550
59.2%
1379
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0550
59.2%
1379
40.8%

propertyCategory_Commercial
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
780 
1
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

Length

2025-10-17T17:58:13.454926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:13.533926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0780
84.0%
1149
 
16.0%

propertyCategory_Residential
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
780 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%

Length

2025-10-17T17:58:13.631243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:13.711478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1780
84.0%
0149
 
16.0%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
708 
0
221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

Length

2025-10-17T17:58:13.808304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:13.882770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1708
76.2%
0221
 
23.8%

propertySubType_Office
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
848 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

Length

2025-10-17T17:58:13.976249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:14.057749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0848
91.3%
181
 
8.7%

propertySubType_Others(very rare)
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
915 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

Length

2025-10-17T17:58:14.165788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:14.261879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0915
98.5%
114
 
1.5%

propertySubType_Residential - Attached Houses
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
869 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Length

2025-10-17T17:58:14.360702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:14.439098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0869
93.5%
160
 
6.5%

propertySubType_Retail
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
0
863 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Length

2025-10-17T17:58:14.539443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T17:58:14.617895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0863
92.9%
166
 
7.1%

Is sqm outlier
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
856 
True
 
73
ValueCountFrequency (%)
False856
92.1%
True73
 
7.9%
2025-10-17T17:58:14.675590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is Price/Sqm outlier
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
781 
True
148 
ValueCountFrequency (%)
False781
84.1%
True148
 
15.9%
2025-10-17T17:58:14.736316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is Price(EGP) outlier
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
812 
True
117 
ValueCountFrequency (%)
False812
87.4%
True117
 
12.6%
2025-10-17T17:58:14.799345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is BuildingNumber outlier
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
794 
True
135 
ValueCountFrequency (%)
False794
85.5%
True135
 
14.5%
2025-10-17T17:58:14.878191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is FloorNumber outlier
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
881 
True
 
48
ValueCountFrequency (%)
False881
94.8%
True48
 
5.2%
2025-10-17T17:58:14.942292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is UnitNumber outlier
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
752 
True
177 
ValueCountFrequency (%)
False752
80.9%
True177
 
19.1%
2025-10-17T17:58:15.001768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

log(sqm)
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2339171
Minimum2.7080502
Maximum6.8977049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:15.117212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7080502
5-th percentile4.4783692
Q14.9487599
median5.32301
Q35.4806389
95-th percentile5.986452
Maximum6.8977049
Range4.1896547
Interquartile range (IQR)0.53187903

Descriptive statistics

Standard deviation0.50401899
Coefficient of variation (CV)0.09629862
Kurtosis1.9395377
Mean5.2339171
Median Absolute Deviation (MAD)0.21102219
Skewness-0.49296433
Sum4862.309
Variance0.25403515
MonotonicityNot monotonic
2025-10-17T17:58:15.276519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.53109298352
 
5.6%
5.52942908831
 
3.3%
5.33271879327
 
2.9%
5.44241771121
 
2.3%
5.98645200514
 
1.5%
4.897839813
 
1.4%
5.17343418413
 
1.4%
6.0161571612
 
1.3%
5.17048399511
 
1.2%
4.8675344511
 
1.2%
Other values (381)724
77.9%
ValueCountFrequency (%)
2.7080502011
 
0.1%
3.1135153091
 
0.1%
3.2958368661
 
0.1%
3.5263605251
 
0.1%
3.5835189381
 
0.1%
3.6109179131
 
0.1%
3.6635616462
0.2%
3.7376696181
 
0.1%
3.7954891891
 
0.1%
3.806662493
0.3%
ValueCountFrequency (%)
6.8977049431
 
0.1%
6.7923444271
 
0.1%
6.7569323891
 
0.1%
6.7452363491
 
0.1%
6.6511842021
 
0.1%
6.6066501864
0.4%
6.5847913921
 
0.1%
6.5395859561
 
0.1%
6.5250296581
 
0.1%
6.414933281
 
0.1%

log(price)
Real number (ℝ)

High correlation 

Distinct795
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.123624
Minimum14.004477
Maximum18.612317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:15.429335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.004477
5-th percentile14.917466
Q115.734196
median16.09932
Q316.410691
95-th percentile17.468519
Maximum18.612317
Range4.6078405
Interquartile range (IQR)0.67649431

Descriptive statistics

Standard deviation0.69581834
Coefficient of variation (CV)0.043155206
Kurtosis0.49376347
Mean16.123624
Median Absolute Deviation (MAD)0.33804266
Skewness0.41208014
Sum14978.847
Variance0.48416316
MonotonicityNot monotonic
2025-10-17T17:58:15.590830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.895671679
 
1.0%
15.86924966
 
0.6%
16.248685224
 
0.4%
15.404745764
 
0.4%
15.83520214
 
0.4%
14.815629074
 
0.4%
14.796936764
 
0.4%
15.688081673
 
0.3%
15.207538453
 
0.3%
15.62674583
 
0.3%
Other values (785)885
95.3%
ValueCountFrequency (%)
14.004476661
0.1%
14.287387431
0.1%
14.31567961
0.1%
14.324133761
0.1%
14.40883741
0.1%
14.544507391
0.1%
14.6396861
0.1%
14.668648471
0.1%
14.669925891
0.1%
14.683870761
0.1%
ValueCountFrequency (%)
18.612317121
0.1%
18.18428731
0.1%
18.014245171
0.1%
18.012366581
0.1%
18.008613892
0.2%
18.006739821
0.1%
17.999177711
0.1%
17.995375112
0.2%
17.99346071
0.1%
17.853725591
0.1%

log(price/sqm)
Real number (ℝ)

High correlation 

Distinct818
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.889707
Minimum9.5901676
Maximum13.159264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2025-10-17T17:58:15.735071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.5901676
5-th percentile10.086422
Q110.393129
median10.786634
Q311.080941
95-th percentile12.249325
Maximum13.159264
Range3.5690962
Interquartile range (IQR)0.68781225

Descriptive statistics

Standard deviation0.65680761
Coefficient of variation (CV)0.060314533
Kurtosis0.42653332
Mean10.889707
Median Absolute Deviation (MAD)0.35095634
Skewness1.0231145
Sum10116.538
Variance0.43139624
MonotonicityNot monotonic
2025-10-17T17:58:15.896095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.028137229
 
1.0%
10.392786056
 
0.6%
11.53956744
 
0.4%
10.028137334
 
0.4%
10.392784384
 
0.4%
10.361320654
 
0.4%
10.089328953
 
0.3%
10.089411563
 
0.3%
10.66796533
 
0.3%
10.382451143
 
0.3%
Other values (808)886
95.4%
ValueCountFrequency (%)
9.5901676351
 
0.1%
9.6174906061
 
0.1%
9.8777444181
 
0.1%
9.9133593011
 
0.1%
9.9294023061
 
0.1%
9.9627542981
 
0.1%
9.9805037081
 
0.1%
9.9903970132
0.2%
10.009444981
 
0.1%
10.009445023
0.3%
ValueCountFrequency (%)
13.159263851
0.1%
12.86547991
0.1%
12.843824561
0.1%
12.822573521
0.1%
12.74591471
0.1%
12.718134791
0.1%
12.711909861
0.1%
12.710008921
0.1%
12.704057891
0.1%
12.700059731
0.1%

Interactions

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2025-10-17T17:58:01.129900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T17:58:02.307009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-17T17:58:16.145269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Bathroom_0Bathroom_1Bathroom_2Bathroom_above 2Bedroom_0Bedroom_1Bedroom_2Bedroom_3Bedroom_above 3BuildingNumberFloorNumberHighFloorIs BuildingNumber outlierIs FloorNumber outlierIs Price(EGP) outlierIs Price/Sqm outlierIs UnitNumber outlierIs sqm outlierLowFloorMidFloorPrice(EGP)Price/SqmUnitNumbercity_Cairocity_Mansouracity_Mersa Matruhcity_New Administrative Capitalcity_New Cairocity_North Coastcity_Old Cairogovernorate_Alexandriagovernorate_Cairogovernorate_Dakahliagovernorate_Matrouhhas_building_facilitieshas_facilitieshas_green_viewhas_main_facilitieshas_modernhas_other_featureshas_parking_safetyhas_security_featureshas_water_featurelog(price)log(price/sqm)log(sqm)namepropertyCategory_CommercialpropertyCategory_ResidentialpropertySubType_Apartment (Residential - Multi-Story Units)propertySubType_OfficepropertySubType_Others(very rare)propertySubType_Residential - Attached HousespropertySubType_Retailsqmyear_built
Bathroom_01.0000.4190.3360.6890.3500.2150.1020.3940.0760.1530.1140.0490.0000.0000.0000.1250.0000.0000.1090.0710.2040.1670.2510.1740.0310.2980.0340.1050.0250.0960.3310.2320.0310.5240.1750.1540.1050.1980.2580.1180.0420.3150.2640.4430.3640.2880.8060.3030.3030.2790.2070.0000.0150.1910.3750.205
Bathroom_10.4191.0000.0580.1430.1420.0850.1500.0890.0260.3050.0000.0800.1280.0530.1050.1230.2980.0420.0000.0760.1490.1390.5660.0650.0000.0930.1060.0740.2630.0060.1410.1910.0000.3230.1390.2260.1930.2380.1360.0000.0980.0770.1320.4120.2680.1310.6310.1240.1240.1350.0810.0000.0000.0690.2430.263
Bathroom_20.3360.0581.0000.1120.1110.0440.0000.0000.1680.0000.0270.0000.0600.0000.0300.0950.0840.0230.0220.0000.0000.1070.0450.0450.0700.0480.3720.0000.1990.0000.0820.2160.0700.1680.2360.1480.1630.0950.0650.2820.4440.5140.1830.1250.2890.2020.8020.0960.0960.0300.0590.0000.0210.0490.3190.116
Bathroom_above 20.6890.1430.1121.0000.2400.1510.0160.3800.1650.1490.1740.1170.0630.0650.1030.0000.1090.0000.0800.0000.3450.1140.1800.1110.0740.4660.1940.0350.0000.0580.2330.2680.0740.4960.1700.2560.3680.1240.1580.0000.1100.1310.3280.4930.4160.3010.8610.2040.2040.2020.1350.0000.0350.1280.3880.353
Bedroom_00.3500.1420.1110.2401.0000.1490.1970.4190.1950.1400.2170.1890.1180.1820.3710.6550.2340.1140.0200.1450.4980.7210.1780.5200.1200.0710.1770.4700.4640.2970.2250.5090.1200.2230.0180.0730.0580.0880.0000.0880.2520.0750.2670.5770.7710.4730.9410.8740.8740.6630.6110.0000.1200.5540.2900.382
Bedroom_10.2150.0850.0440.1510.1491.0000.1220.2680.1210.0620.0830.0850.0540.0570.0630.1190.0980.0800.0000.0780.1110.2240.1010.0700.0690.0990.0500.0790.1670.0150.4270.0510.0690.2520.2320.1280.2160.1720.5420.1560.0910.0820.2510.4350.2260.7000.5640.1310.1310.1170.0860.0000.0000.0740.6200.194
Bedroom_20.1020.1500.0000.0160.1970.1221.0000.3490.1610.3570.0000.0580.2220.0090.1200.1550.1900.0860.0270.0760.1850.1890.2010.0850.0410.1010.0840.0850.1470.0410.0000.0410.0410.0970.0000.1890.0210.0290.0780.0000.0180.0000.0510.2990.3180.5370.4370.1650.1650.1880.1060.0000.0700.1030.4290.146
Bedroom_30.3940.0890.0000.3800.4190.2680.3491.0000.3460.1610.0530.0000.0320.0000.0930.2070.0270.1640.0000.0000.2000.2580.1270.2200.1930.1740.0000.1840.0000.1180.1790.2660.1930.3050.0800.0540.1920.0330.2510.1040.1280.0800.2340.3630.2990.4850.6150.3700.3700.1970.2590.0000.2020.2300.4080.382
Bedroom_above 30.0760.0260.1680.1650.1950.1210.1610.3461.0000.0690.0670.0560.0890.0510.0790.1710.0890.0610.0000.0000.1190.2090.3060.0970.0000.1370.0370.0840.1790.0400.1860.0820.0000.0590.0190.0940.0430.0670.0000.0870.0000.0690.1120.2250.3600.3920.4080.1710.1710.1640.1160.0540.0550.1020.4720.112
BuildingNumber0.1530.3050.0000.1490.1400.0620.3570.1610.0691.0000.0090.0730.8520.0000.0930.1170.5140.0560.1190.141-0.114-0.0250.3880.0350.3260.1020.5810.0540.3150.0000.1620.3770.3260.3160.2120.3420.3230.3370.1390.3650.1430.0000.299-0.114-0.025-0.1220.4070.1170.1170.2110.0660.0000.3260.058-0.1220.109
FloorNumber0.1140.0000.0270.1740.2170.0830.0000.0530.0670.0091.0000.9960.0270.9960.3390.4400.0770.1830.8120.814-0.0390.1560.0870.3510.2180.3340.1360.2390.2050.0000.1540.1740.2180.1530.1430.0820.2380.3510.1250.1200.2590.4490.105-0.0390.156-0.1380.3460.2110.2110.2650.2810.0000.2180.273-0.1380.132
HighFloor0.0490.0800.0000.1170.1890.0850.0580.0000.0560.0730.9961.0000.0820.7510.2220.3740.1100.0670.2710.2810.2960.5030.0840.2500.0650.1990.0340.0000.1820.0080.1230.0880.0650.0420.0830.0420.2020.3070.1180.1290.2190.3370.0200.3180.5500.3280.6200.1500.1500.0690.1950.0000.0650.0000.3170.128
Is BuildingNumber outlier0.0000.1280.0600.0630.1180.0540.2220.0320.0890.8520.0270.0821.0000.0830.1010.1550.5190.0330.0000.0490.0990.1890.4780.0980.0110.1390.2940.1090.0310.0850.2080.1280.0110.0000.0000.1890.0750.0480.1180.1820.0000.0640.0440.1710.2820.1470.6630.1560.1560.1150.1180.0520.0000.0780.1700.078
Is FloorNumber outlier0.0000.0530.0000.0650.1820.0570.0090.0000.0510.0000.9960.7510.0831.0000.2240.3960.0890.0000.2030.2110.2770.5170.0350.1410.0400.2240.0850.0600.1550.0000.0730.0140.0400.0490.1310.0000.1580.3280.0690.1410.1650.2950.0530.2730.5180.3360.6290.1120.1120.0480.1040.0000.0400.0220.2430.091
Is Price(EGP) outlier0.0000.1050.0300.1030.3710.0630.1200.0930.0790.0930.3390.2220.1010.2241.0000.6630.1770.1690.0630.0410.9500.7570.1290.0000.0000.2800.0740.4650.3260.0330.1910.1630.0000.0000.1160.2530.0000.1050.1200.1130.0500.3330.0290.9190.7620.2890.8180.3850.3850.3310.4160.0650.0230.0850.3330.172
Is Price/Sqm outlier0.1250.1230.0950.0000.6550.1190.1550.2070.1710.1170.4400.3740.1550.3960.6631.0000.2050.0270.0550.1410.7280.9920.1530.4720.1030.1730.1490.4830.4270.0460.2210.3920.1030.1210.0100.1000.0870.3430.1630.0000.3090.2410.1550.7360.9630.4730.9490.7030.7030.5330.5580.0000.0910.3650.3230.264
Is UnitNumber outlier0.0000.2980.0840.1090.2340.0980.1900.0270.0890.5140.0770.1100.5190.0890.1770.2051.0000.1220.0350.1110.2080.2580.8020.1190.1170.1350.1520.1320.1990.0560.2470.0350.1170.3180.0000.3260.0380.2290.1440.0360.0580.0820.2320.2740.4050.2370.7820.2060.2060.2590.1410.0000.1170.1250.2160.290
Is sqm outlier0.0000.0420.0230.0000.1140.0800.0860.1640.0610.0560.1830.0670.0330.0000.1690.0270.1221.0000.1230.1700.3820.1070.0800.0170.6470.0400.0000.0700.2750.0000.1430.0750.6470.1120.1730.2600.0600.0000.1190.2130.0510.1520.1180.4060.3830.7910.7350.0950.0950.2720.0760.3070.4680.0260.8560.488
LowFloor0.1090.0000.0220.0800.0200.0000.0270.0000.0000.1190.8120.2710.0000.2030.0630.0550.0350.1231.0000.8390.1920.1620.0480.0310.2860.0500.0940.0720.0730.0000.0730.1030.2860.0000.0670.0390.1480.0650.0000.0560.0340.1560.0730.2010.2430.1780.4210.0680.0680.2650.1170.0870.2860.2500.2180.185
MidFloor0.0710.0760.0000.0000.1450.0780.0760.0000.0000.1410.8140.2810.0490.2110.0410.1410.1110.1700.8391.0000.1700.2190.0000.1880.2390.1730.1240.0550.1830.0430.1500.0420.2390.0480.0000.0000.0090.0930.0730.0000.0660.0000.0450.2100.2870.2670.4240.1610.1610.3100.0000.0630.2390.2520.2290.158
Price(EGP)0.2040.1490.0000.3450.4980.1110.1850.2000.119-0.114-0.0390.2960.0990.2770.9500.7280.2080.3820.1920.1701.0000.6750.1200.2360.0000.4730.1040.5400.4000.0250.2840.2200.0000.1860.2030.2890.1760.2300.1920.1700.1050.4810.1501.0000.6750.5100.5960.5390.5390.4940.4820.1590.0000.4800.5100.218
Price/Sqm0.1670.1390.1070.1140.7210.2240.1890.2580.209-0.0250.1560.5030.1890.5170.7570.9920.2580.1070.1620.2190.6751.0000.2070.6160.1200.3540.1650.6970.4250.0000.2060.4290.1200.2070.2950.2490.2400.4580.2090.3450.5230.3650.2320.6751.000-0.1700.5310.7900.7900.6040.6940.0000.0490.704-0.1700.176
UnitNumber0.2510.5660.0450.1800.1780.1010.2010.1270.3060.3880.0870.0840.4780.0350.1290.1530.8020.0800.0480.0000.1200.2071.0000.0730.0720.1290.2700.0860.3560.0000.1900.2610.0720.4240.2030.3570.3090.4560.1940.2450.0880.0680.3550.1200.2070.0290.3820.1540.1540.2060.0960.0000.0720.0790.0290.253
city_Cairo0.1740.0650.0450.1110.5200.0700.0850.2200.0970.0350.3510.2500.0980.1410.0000.4720.1190.0170.0310.1880.2360.6160.0731.0000.0510.0880.0990.0600.2560.0000.1290.3860.0510.2130.1970.1260.2410.2920.0550.1750.4990.0630.2190.2560.6230.6310.9850.6000.6000.4680.3560.0000.0510.4250.4230.511
city_Mansoura0.0310.0000.0700.0740.1200.0690.0410.1930.0000.3260.2180.0650.0110.0400.0000.1030.1170.6470.2860.2390.0000.1200.0720.0511.0000.0870.0980.0600.2540.0000.1270.1700.9910.2110.3350.2400.2250.1390.1240.3760.0810.0620.2170.2370.4300.7100.9850.1040.1040.4640.0660.3440.7950.0550.8050.648
city_Mersa Matruh0.2980.0930.0480.4660.0710.0990.1010.1740.1370.1020.3340.1990.1390.2240.2800.1730.1350.0400.0500.1730.4730.3540.1290.0880.0871.0000.1510.0990.3710.0330.1910.2520.0870.4530.1380.1810.2020.0360.1200.0360.1270.2970.2170.4780.4870.3130.8210.0600.0600.0000.1070.0070.0000.2270.3610.163
city_New Administrative Capital0.0340.1060.3720.1940.1770.0500.0840.0000.0370.5810.1360.0340.2940.0850.0740.1490.1520.0000.0940.1240.1040.1650.2700.0990.0980.1511.0000.1110.4100.0420.2110.6130.0980.3420.0940.1350.1740.2290.0980.2590.2530.2580.3510.1840.4010.2300.9850.1590.1590.1700.1190.0220.0000.0800.1910.258
city_New Cairo0.1050.0740.0000.0350.4700.0790.0850.1840.0840.0540.2390.0000.1090.0600.4650.4830.1320.0700.0720.0550.5400.6970.0860.0600.0600.0990.1111.0000.2810.0000.1420.4230.0600.2340.2030.1600.1430.0000.0000.2430.0000.0710.2400.5750.7470.2790.9850.5140.5140.3900.6450.0000.0600.0000.1210.174
city_North Coast0.0250.2630.1990.0000.4640.1670.1470.0000.1790.3150.2050.1820.0310.1550.3260.4270.1990.2750.0730.1830.4000.4250.3560.2560.2540.3710.4100.2811.0000.1400.5220.6680.2540.3220.0570.2050.1480.0570.1750.5160.2690.2480.4020.4760.4780.5210.9170.4290.4290.5450.3010.0900.2540.2680.3630.456
city_Old Cairo0.0960.0060.0000.0580.2970.0150.0410.1180.0400.0000.0000.0080.0850.0000.0330.0460.0560.0000.0000.0430.0250.0000.0000.0000.0000.0330.0420.0000.1401.0000.0620.2140.0000.1140.1870.1310.1230.0700.2890.2110.0280.0000.1680.5240.3410.2140.9850.0460.0460.0690.0090.0000.0000.0000.1520.077
governorate_Alexandria0.3310.1410.0820.2330.2250.4270.0000.1790.1860.1620.1540.1230.2080.0730.1910.2210.2470.1430.0730.1500.2840.2060.1900.1290.1270.1910.2110.1420.5220.0621.0000.3480.1270.4280.3860.0000.0330.2880.4090.3420.1800.1460.0440.4900.2710.4360.9570.2220.2220.2860.1530.0420.1270.1350.3250.324
governorate_Cairo0.2320.1910.2160.2680.5090.0510.0410.2660.0820.3770.1740.0880.1280.0140.1630.3920.0350.0750.1030.0420.2200.4290.2610.3860.1700.2520.6130.4230.6680.2140.3481.0000.1700.5590.0000.0150.1340.0000.0000.3160.4380.0870.4740.3210.4870.4670.9850.4710.4710.3140.4510.0150.1120.1570.2550.438
governorate_Dakahlia0.0310.0000.0700.0740.1200.0690.0410.1930.0000.3260.2180.0650.0110.0400.0000.1030.1170.6470.2860.2390.0000.1200.0720.0510.9910.0870.0980.0600.2540.0000.1270.1701.0000.2110.3350.2400.2250.1390.1240.3760.0810.0620.2170.2370.4300.7100.9850.1040.1040.4640.0660.3440.7950.0550.8050.648
governorate_Matrouh0.5240.3230.1680.4960.2230.2520.0970.3050.0590.3160.1530.0420.0000.0490.0000.1210.3180.1120.0000.0480.1860.2070.4240.2130.2110.4530.3420.2340.3220.1140.4280.5590.2111.0000.1520.0620.0450.3450.2420.2000.2090.0640.5110.3670.4400.4000.9660.1960.1960.2900.2510.0680.1660.0000.4000.602
has_building_facilities0.1750.1390.2360.1700.0180.2320.0000.0800.0190.2120.1430.0830.0000.1310.1160.0100.0000.1730.0670.0000.2030.2950.2030.1970.3350.1380.0940.2030.0570.1870.3860.0000.3350.1521.0000.3040.2860.0510.2590.3490.0000.0710.1280.3440.3470.3510.8340.0000.0000.1810.1650.1100.2800.2060.3820.565
has_facilities0.1540.2260.1480.2560.0730.1280.1890.0540.0940.3420.0820.0420.1890.0000.2530.1000.3260.2600.0390.0000.2890.2490.3570.1260.2400.1810.1350.1600.2050.1310.0000.0150.2400.0620.3041.0000.1570.2310.3170.2150.0880.2320.1400.3430.3400.3560.8000.0000.0000.1590.1450.0640.1870.1090.3430.136
has_green_view0.1050.1930.1630.3680.0580.2160.0210.1920.0430.3230.2380.2020.0750.1580.0000.0870.0380.0600.1480.0090.1760.2400.3090.2410.2250.2020.1740.1430.1480.1230.0330.1340.2250.0450.2860.1571.0000.1160.3580.1830.0110.0000.3310.3620.3720.2630.8470.0320.0320.1670.0320.0340.1810.0000.2510.241
has_main_facilities0.1980.2380.0950.1240.0880.1720.0290.0330.0670.3370.3510.3070.0480.3280.1050.3430.2290.0000.0650.0930.2300.4580.4560.2920.1390.0360.2290.0000.0570.0700.2880.0000.1390.3450.0510.2310.1161.0000.1980.3820.2120.1680.3300.3440.5730.3650.8070.0960.0960.0000.0000.0000.1390.1400.1910.219
has_modern0.2580.1360.0650.1580.0000.5420.0780.2510.0000.1390.1250.1180.1180.0690.1200.1630.1440.1190.0000.0730.1920.2090.1940.0550.1240.1200.0980.0000.1750.2890.4090.0000.1240.2420.2590.3170.3580.1981.0000.2120.1750.1420.3140.4820.2270.4370.7140.1110.1110.1510.1480.0400.0530.0000.3750.183
has_other_features0.1180.0000.2820.0000.0880.1560.0000.1040.0870.3650.1200.1290.1820.1410.1130.0000.0360.2130.0560.0000.1700.3450.2450.1750.3760.0360.2590.2430.5160.2110.3420.3160.3760.2000.3490.2150.1830.3820.2121.0000.1300.1090.2910.2660.4680.3740.8820.0000.0000.2450.2060.1090.3200.1830.3530.500
has_parking_safety0.0420.0980.4440.1100.2520.0910.0180.1280.0000.1430.2590.2190.0000.1650.0500.3090.0580.0510.0340.0660.1050.5230.0880.4990.0810.1270.2530.0000.2690.0280.1800.4380.0810.2090.0000.0880.0110.2120.1750.1301.0000.3580.2660.1770.5400.5470.8800.3070.3070.1980.0940.0000.0810.3010.3740.220
has_security_features0.3150.0770.5140.1310.0750.0820.0000.0800.0690.0000.4490.3370.0640.2950.3330.2410.0820.1520.1560.0000.4810.3650.0680.0630.0620.2970.2580.0710.2480.0000.1460.0870.0620.0640.0710.2320.0000.1680.1420.1090.3581.0000.0550.4680.4520.4130.9490.1200.1200.1580.0780.0000.0620.0670.5270.178
has_water_feature0.2640.1320.1830.3280.2670.2510.0510.2340.1120.2990.1050.0200.0440.0530.0290.1550.2320.1180.0730.0450.1500.2320.3550.2190.2170.2170.3510.2400.4020.1680.0440.4740.2170.5110.1280.1400.3310.3300.3140.2910.2660.0551.0000.3060.4010.5000.8000.3680.3680.4410.2570.0710.1720.2290.4570.426
log(price)0.4430.4120.1250.4930.5770.4350.2990.3630.225-0.114-0.0390.3180.1710.2730.9190.7360.2740.4060.2010.2101.0000.6750.1200.2560.2370.4780.1840.5750.4760.5240.4900.3210.2370.3670.3440.3430.3620.3440.4820.2660.1770.4680.3061.0000.6750.5100.6000.5790.5790.5570.5140.2120.2270.4340.5100.364
log(price/sqm)0.3640.2680.2890.4160.7710.2260.3180.2990.360-0.0250.1560.5500.2820.5180.7620.9630.4050.3830.2430.2870.6751.0000.2070.6230.4300.4870.4010.7470.4780.3410.2710.4870.4300.4400.3470.3400.3720.5730.2270.4680.5400.4520.4010.6751.000-0.1700.6170.8370.8370.7010.7550.1080.3410.811-0.1700.410
log(sqm)0.2880.1310.2020.3010.4730.7000.5370.4850.392-0.122-0.1380.3280.1470.3360.2890.4730.2370.7910.1780.2670.510-0.1700.0290.6310.7100.3130.2300.2790.5210.2140.4360.4670.7100.4000.3510.3560.2630.3650.4370.3740.5470.4130.5000.510-0.1701.0000.5210.4730.4730.5000.2490.6360.6240.5201.0000.359
name0.8060.6310.8020.8610.9410.5640.4370.6150.4080.4070.3460.6200.6630.6290.8180.9490.7820.7350.4210.4240.5960.5310.3820.9850.9850.8210.9850.9850.9170.9850.9570.9850.9850.9660.8340.8000.8470.8070.7140.8820.8800.9490.8000.6000.6170.5211.0000.9770.9770.9670.9110.3290.8680.8750.4500.860
propertyCategory_Commercial0.3030.1240.0960.2040.8740.1310.1650.3700.1710.1170.2110.1500.1560.1120.3850.7030.2060.0950.0680.1610.5390.7900.1540.6000.1040.0600.1590.5140.4290.0460.2220.4710.1040.1960.0000.0000.0320.0960.1110.0000.3070.1200.3680.5790.8370.4730.9771.0000.9960.7790.7020.0000.1040.6270.2760.383
propertyCategory_Residential0.3030.1240.0960.2040.8740.1310.1650.3700.1710.1170.2110.1500.1560.1120.3850.7030.2060.0950.0680.1610.5390.7900.1540.6000.1040.0600.1590.5140.4290.0460.2220.4710.1040.1960.0000.0000.0320.0960.1110.0000.3070.1200.3680.5790.8370.4730.9770.9961.0000.7790.7020.0000.1040.6270.2760.383
propertySubType_Apartment (Residential - Multi-Story Units)0.2790.1350.0300.2020.6630.1170.1880.1970.1640.2110.2650.0690.1150.0480.3310.5330.2590.2720.2650.3100.4940.6040.2060.4680.4640.0000.1700.3900.5450.0690.2860.3140.4640.2900.1810.1590.1670.0000.1510.2450.1980.1580.4410.5570.7010.5000.9670.7790.7791.0000.5480.2090.4640.4890.4120.419
propertySubType_Office0.2070.0810.0590.1350.6110.0860.1060.2590.1160.0660.2810.1950.1180.1040.4160.5580.1410.0760.1170.0000.4820.6940.0960.3560.0660.1070.1190.6450.3010.0090.1530.4510.0660.2510.1650.1450.0320.0000.1480.2060.0940.0780.2570.5140.7550.2490.9110.7020.7020.5481.0000.0000.0660.0710.1570.298
propertySubType_Others(very rare)0.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0520.0000.0650.0000.0000.3070.0870.0630.1590.0000.0000.0000.3440.0070.0220.0000.0900.0000.0420.0150.3440.0680.1100.0640.0340.0000.0400.1090.0000.0000.0710.2120.1080.6360.3290.0000.0000.2090.0001.0000.0000.0000.7620.129
propertySubType_Residential - Attached Houses0.0150.0000.0210.0350.1200.0000.0700.2020.0550.3260.2180.0650.0000.0400.0230.0910.1170.4680.2860.2390.0000.0490.0720.0510.7950.0000.0000.0600.2540.0000.1270.1120.7950.1660.2800.1870.1810.1390.0530.3200.0810.0620.1720.2270.3410.6240.8680.1040.1040.4640.0660.0001.0000.0550.6910.563
propertySubType_Retail0.1910.0690.0490.1280.5540.0740.1030.2300.1020.0580.2730.0000.0780.0220.0850.3650.1250.0260.2500.2520.4800.7040.0790.4250.0550.2270.0800.0000.2680.0000.1350.1570.0550.0000.2060.1090.0000.1400.0000.1830.3010.0670.2290.4340.8110.5200.8750.6270.6270.4890.0710.0000.0551.0000.2400.213
sqm0.3750.2430.3190.3880.2900.6200.4290.4080.472-0.122-0.1380.3170.1700.2430.3330.3230.2160.8560.2180.2290.510-0.1700.0290.4230.8050.3610.1910.1210.3630.1520.3250.2550.8050.4000.3820.3430.2510.1910.3750.3530.3740.5270.4570.510-0.1701.0000.4500.2760.2760.4120.1570.7620.6910.2401.0000.403
year_built0.2050.2630.1160.3530.3820.1940.1460.3820.1120.1090.1320.1280.0780.0910.1720.2640.2900.4880.1850.1580.2180.1760.2530.5110.6480.1630.2580.1740.4560.0770.3240.4380.6480.6020.5650.1360.2410.2190.1830.5000.2200.1780.4260.3640.4100.3590.8600.3830.3830.4190.2980.1290.5630.2130.4031.000

Missing values

2025-10-17T17:58:04.163322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-17T17:58:04.869229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namesqmyear_builtPrice(EGP)Price/Sqmhas_main_facilitieshas_security_featureshas_parking_safetyhas_building_facilitieshas_other_featuresHighFloorLowFloorMidFloorFloorNumberBuildingNumberUnitNumberhas_modernhas_facilitieshas_green_viewhas_water_featureBedroom_0Bedroom_1Bedroom_2Bedroom_3Bedroom_above 3Bathroom_0Bathroom_1Bathroom_2Bathroom_above 2city_Cairocity_Mansouracity_Mersa Matruhcity_New Administrative Capitalcity_New Cairocity_North Coastcity_Old Cairogovernorate_Alexandriagovernorate_Cairogovernorate_Dakahliagovernorate_MatrouhpropertyCategory_CommercialpropertyCategory_ResidentialpropertySubType_Apartment (Residential - Multi-Story Units)propertySubType_OfficepropertySubType_Others(very rare)propertySubType_Residential - Attached HousespropertySubType_RetailIs sqm outlierIs Price/Sqm outlierIs Price(EGP) outlierIs BuildingNumber outlierIs FloorNumber outlierIs UnitNumber outlierlog(sqm)log(price)log(price/sqm)
0Alamain (Latin District)92.862025659400071010.1228110010016421100010001000000001010000110000FalseFalseFalseFalseFalseFalse4.53109315.70167111.170578
1Beachfront Tower - B1398.00202566104000166090.4523000011009110910111000100001001000000010110000TrueTrueTrueFalseFalseFalse5.98645218.00674012.020288
2PODIA93.00202512824000137892.47310110110013713040010100001000100000001001001000FalseTrueFalseFalseTrueFalse4.53259916.36682911.834229
3Mazarine Apartment252.0020251285700051019.84131100000131515310111000100001001000000010110000FalseFalseFalseFalseFalseFalse5.52942916.36939910.839970
4Alamain (Latin District)209.422025872100041643.5870110010101120011000101000000001010000110000FalseFalseFalseFalseFalseFalse5.34434215.98124410.636903
5Latin City139.002025668800048115.107901001010133021070001001001000000001000010110000FalseFalseFalseFalseFalseTrue4.93447415.71582510.781352
6Downtown commercial272.00202532696000120205.882411001010088150000100001000001000000011000001FalseTrueTrueFalseFalseFalse5.60580217.30276311.696961
7Beachfront Tower - B1221.00202528507000128990.95020000110016111720111100001000001000000010110000FalseTrueTrueFalseTrueFalse5.39816317.16566011.767498
8Downtown commercial319.00202536428000114194.357411001010088130000100001000001000000011000001FalseTrueTrueFalseFalseFalse5.76519117.41084811.645657
9Downtown Extension173.002022836800048369.942211011001311110000001000001000001000010110000FalseFalseFalseFalseFalseFalse5.15329215.93992510.786634
namesqmyear_builtPrice(EGP)Price/Sqmhas_main_facilitieshas_security_featureshas_parking_safetyhas_building_facilitieshas_other_featuresHighFloorLowFloorMidFloorFloorNumberBuildingNumberUnitNumberhas_modernhas_facilitieshas_green_viewhas_water_featureBedroom_0Bedroom_1Bedroom_2Bedroom_3Bedroom_above 3Bathroom_0Bathroom_1Bathroom_2Bathroom_above 2city_Cairocity_Mansouracity_Mersa Matruhcity_New Administrative Capitalcity_New Cairocity_North Coastcity_Old Cairogovernorate_Alexandriagovernorate_Cairogovernorate_Dakahliagovernorate_MatrouhpropertyCategory_CommercialpropertyCategory_ResidentialpropertySubType_Apartment (Residential - Multi-Story Units)propertySubType_OfficepropertySubType_Others(very rare)propertySubType_Residential - Attached HousespropertySubType_RetailIs sqm outlierIs Price/Sqm outlierIs Price(EGP) outlierIs BuildingNumber outlierIs FloorNumber outlierIs UnitNumber outlierlog(sqm)log(price)log(price/sqm)
919Mazarine Commercial217.00202524308000112018.433211001010144111100100001000001000000011000001FalseFalseTrueFalseFalseFalse5.37989717.00631611.626419
920Zahya392.002024933000023801.0204110100100330100000101000010000000100100010TrueFalseFalseFalseFalseFalse5.97126216.04874610.077484
921AlMaqsad Residences134.002025457200034119.40301100100178181731010000100001000100001000110000FalseFalseFalseTrueFalseTrue4.89784015.33546110.437622
922T- Residences130.002024266825920525.06921100000164620000000100100000001000010110000FalseFalseFalseFalseFalseFalse4.86753414.7969379.929402
923Alamain (Latin District)125.912025679200053943.2928110010013731100001001000000001000010110000FalseFalseFalseFalseFalseFalse4.83556715.73125610.895689
924Alamain (Latin District)177.472025567800031994.1399110010100450011000101000000001010000110000FalseFalseFalseFalseFalseFalse5.17880215.55211010.373308
925Jade Park692.0020252529200036549.1329101100016110100000010010000100001000110000TrueFalseTrueFalseFalseFalse6.53958617.04599910.506413
926Mamsha Avenue171.002025613700035888.888911001001415150110000101000000100001000110000FalseFalseFalseFalseFalseFalse5.14166415.62984710.488183
927Alamain (Latin District)207.582025593300028581.7516110010014651100000101000000001010000110000FalseFalseFalseFalseFalseFalse5.33551715.59604110.260524
928PODIA42.0020255446000129666.666701101001474160010100001000100000001001000100FalseTrueFalseFalseFalseFalse3.73767015.51039211.772722